Done.
[ NOTE, FIGURE ]: Red horizontal line indicates a signed R^2 of 0.9
Setting soft-thresholding power to: 15.
Power-transforming the gene-gene similarity matrix...Done.
---------------------------------------------------
4. Convert into topological overlap matrix (dissTOM)
---------------------------------------------------
Creating dissTOM...Done.
Performing hierarchical clustering on dissTOM...Done.
---------------------------------------------------
5. Identify modules (clusters)
---------------------------------------------------
Merging modules that have a correlation ≥ 0.9 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
blue cyan darkgreen darkgrey darkorange
843 173 342 174 66
darkred darkturquoise green lightgreen lightyellow
82 78 257 90 347
magenta turquoise yellow
251 234 163
Cutoff used: 0.9
Number of modules identified: 13
Calculating module-module similarity based
on module-eigengene-expression...Done.
Tidying module names...Done.
Plotting adjacency matrix for module-module similarity...
Done.
[ NOTE, FIGURE ]: Red horizontal line indicates a signed R^2 of 0.9
Setting soft-thresholding power to: 15.
Power-transforming the gene-gene similarity matrix...Done.
---------------------------------------------------
4. Convert into topological overlap matrix (dissTOM)
---------------------------------------------------
Creating dissTOM...Done.
Performing hierarchical clustering on dissTOM...Done.
---------------------------------------------------
5. Identify modules (clusters)
---------------------------------------------------
Merging modules that have a correlation ≥ 0.9 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black blue brown cyan darkgreen
149 190 181 118 90
darkgrey darkorange darkred darkturquoise green
79 65 91 88 165
greenyellow grey60 lightcyan lightgreen lightyellow
133 105 108 104 104
magenta midnightblue orange paleturquoise pink
144 112 71 50 148
purple red royalblue saddlebrown salmon
134 163 96 60 118
skyblue steelblue tan turquoise white
61 52 121 238 63
yellow
180
Merging modules that have a correlation ≥ 0.85 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black blue brown cyan darkgreen
149 190 181 118 223
darkgrey darkorange darkred darkturquoise green
200 65 91 88 165
grey60 lightcyan lightgreen lightyellow magenta
105 108 167 104 144
midnightblue orange paleturquoise pink purple
112 71 50 148 134
red royalblue saddlebrown salmon skyblue
163 96 60 118 61
steelblue turquoise yellow
52 238 180
Merging modules that have a correlation ≥ 0.8 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black blue brown cyan darkgreen
240 190 269 118 461
darkgrey darkorange green grey60 lightcyan
200 65 165 105 108
lightgreen lightyellow magenta midnightblue orange
167 104 196 112 71
paleturquoise pink purple red royalblue
168 148 134 163 96
saddlebrown skyblue yellow
60 61 180
Merging modules that have a correlation ≥ 0.75 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black brown cyan darkgreen darkorange
240 269 118 857 65
grey60 lightcyan lightgreen lightyellow orange
105 108 634 104 71
paleturquoise pink purple red royalblue
168 148 134 163 157
saddlebrown yellow
60 180
Merging modules that have a correlation ≥ 0.7 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black blue brown cyan darkgreen darkorange
240 634 403 118 857 65
grey60 lightcyan lightyellow orange pink red
105 276 104 71 148 163
royalblue saddlebrown yellow
157 60 180
Cutoff used: 0.7
Number of modules identified: 15
Calculating module-module similarity based
on module-eigengene-expression...Done.
Tidying module names...Done.
Plotting adjacency matrix for module-module similarity...
Done.
[ NOTE, FIGURE ]: Red horizontal line indicates a signed R^2 of 0.9
Setting soft-thresholding power to: 15.
Power-transforming the gene-gene similarity matrix...Done.
---------------------------------------------------
4. Convert into topological overlap matrix (dissTOM)
---------------------------------------------------
Creating dissTOM...Done.
Performing hierarchical clustering on dissTOM...Done.
---------------------------------------------------
5. Identify modules (clusters)
---------------------------------------------------
Merging modules that have a correlation ≥ 0.9 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black blue brown cyan darkgreen
178 252 238 126 66
darkred darkturquoise green greenyellow grey60
69 53 220 141 93
lightcyan lightgreen lightyellow magenta midnightblue
93 87 80 172 99
pink purple red royalblue salmon
175 170 214 71 131
tan turquoise yellow
137 364 232
Merging modules that have a correlation ≥ 0.85 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black blue brown cyan darkgreen
542 424 413 126 66
darkred darkturquoise green greenyellow grey60
69 53 220 141 93
lightcyan lightgreen lightyellow midnightblue purple
93 224 80 99 301
red royalblue yellow
214 71 232
Merging modules that have a correlation ≥ 0.8 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black blue brown cyan darkgreen
542 424 413 126 159
darkred darkturquoise green greenyellow lightcyan
149 53 434 373 164
lightgreen midnightblue purple
224 99 301
Cutoff used: 0.8
Number of modules identified: 13
Calculating module-module similarity based
on module-eigengene-expression...Done.
Tidying module names...Done.
Plotting adjacency matrix for module-module similarity...
trash <- purrr::map( sample.names,function(x) {writeLines(" ##################################################### How many genes are in each of my geneset of interest? #####################################################")## MAKE YOUR LIST OF GENES OF INTEREST ### LIST ONE - WGCNA modules list1 <- l_module_genes[[x]]sapply(list1, length) |>print()## LIST TWO - rhythmic genes list2 <- l_rhy_genes[[x]]sapply(list2, length) |>print()## CHECK FOR OVERLAP# define size of genome size =length(unique(c(unlist(list1), unlist(list2))))# make a GOM object gom.1v2 <- GeneOverlap::newGOM( list2, list1,genome.size = size ) GeneOverlap::drawHeatmap( gom.1v2,adj.p =TRUE,cutoff=0.05,what="odds.ratio",# what="Jaccard",log.scale = T,note.col ="black",grid.col ="Oranges" ) })
#####################################################
How many genes are in each of my geneset of interest?
#####################################################
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
843 257 163 78 251 347 342 82 234 173 66 174 90
ARS empJTK GeneCycle JTK meta2d RAIN
141 405 247 110 156 363
#####################################################
How many genes are in each of my geneset of interest?
#####################################################
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
634 240 65 118 857 276 180 157 105 163 71 60 403 148 104
ARS empJTK GeneCycle JTK meta2d RAIN
468 594 293 229 484 683
#####################################################
How many genes are in each of my geneset of interest?
#####################################################
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
149 224 164 373 301 159 53 424 542 413 126 99 434
ARS GeneCycle meta2d
252 241 75